2007 IEEE Congress on Evolutionary Computation (CEC)

	Accepted Special Sessions
	1. Analysis of Genetic Representations and Operators 2. Ant Colony Optimization 3. Computational Systems Biology 4. Constraint-Handling Techniques Used in Evolutionary Algorithms 5. Differential Evolution 6. Evolutionary Algorithms Based on Probabilistic Models 7. Evolutionary Algorithms in Forecasting Support Systems 8. EC at Work - Generating Value with Evolutionary Computation 9. Evolutionary Computation for Electronic Design Automation 10. Evolutionary Computation for Expensive Optimization Problems 11. Evolutionary Computation for Power Engineering Applications 12. Evolutionary Computation for Process Optimisation 13. Evolutionary Computation in Bioinformatics and Computational Biology 14. Evolutionary Computation in Cryptology and Computer Security 15. Evolutionary Computation in Defence Applications 16. Evolutionary Computation in Dynamic and Uncertain Environments 17. Evolutionary Computation in Finance and Economics 18. Evolutionary Computation in Games 19. Evolutionary Computation in Manufacturing Systems and Robotics 20. Evolutionary Computation in Space 21. Evolutionary Computation in Structural and Multidisciplinary Optimization 22. Evolutionary Computer Vision 23. Evolutionary Computing for Decentralized Systems 24. Evolutionary Design 25. Evolutionary Multiobjective Optimization 26. Evolutionary Planning and Scheduling 27. Evolved Art and Music 28. Genetics-Based Machine Learning 29. Incremental Strategies to Computational Intelligence 30. Linkage in Evolutionary Computation 31. Memetic Algorithms 32. Molecular Computing for Information Processing and Self-Assembly 33. New Particle Swarm Optimization Methods 34. Organic Computing – An Approach to Controlled Emergence? 35. Performance Assessment of Multi-Objective Optimization Algorithms 36. Quantum Computing and Quantum Computational Intelligence 37. Recent Developments in Artificial Immune Systems 38. Theoretical Foundations of Evolutionary Computation

	Abstract
	1. Title: Analysis of Genetic Representations and Operators Organizers: Francisco Baptista Pereira and Jorge Tavares This special session will focus on the analysis and design of Genetic Representations and Operators in Evolutionary Algorithms (EAs). In the past few years, EAs have been successfully applied to a large number of optimization problems. Nevertheless, most of the research deals with applying an EA to a new problem or applying a new EA to an existing one. In most of these studies, the focus is on showing how the EA was able to solve the problem (or find better solutions) and not in why it was able to solve it. The choice of a genetic representation and the associated operators cannot be made independently of each other, as both of them are crucial to the efficiency of the optimisation algorithm. The question whether a certain representation leads to better performing EAs than an alternative representation (and the respective operators) is very important to know why an EA is able to successfully solve a problem. Even though the importance of choosing proper representation-operators is widely recognized, there is little available knowledge in order to understand and guide the construction of successful and efficient EAs. The aim of this Special Session is to promote a widespread discussion about this topic. The themes of the track include (although not limited to them): Representation techniques and evolutionary operators; Theoretical and empirical properties of representations and/or operators; Predictive performance measures; Search space analysis; Promising directions for future research. Top
	2. Title: Ant Colony Optimization Organizer: Hai-Bin Duan Ant Colony Optimization (ACO) is a population-based stochastic optimization technique mimicking the foraging behavior of real ants which enables them to find shortest paths between nest and food sources. In recent years it has gained increasing popularity in the Evolutionary Computation research community, largely due to the fact that ACO has been shown to be an effective optimization method for solving difficult optimization problems. The aim of this session is to consolidate state of the art in ACO, but also to encourage the publication of more ‘mould breaking’ ACO research. Particular encouragement is given to the submission of applications of ACO in industrial settings and advances in theoretical aspects of ACO. To maintain the inter-disciplinarity of ACO, the session encourages the submission of ant colony modeling results using both computational and mathematical modeling techniques that can inform the development of ACO. In addition, we welcome position papers which provide a discussion of current “hot” topics in the area, for example outlining future directions for the area, or discuss the current state-of-the art. This special session aims at providing a forum for reviewing of current state-of-art linkage ACO, exchanging of ideas and viewpoints on linkage, as well as discussing the future directions. Papers are invited for submission on unpublished work in the following (but not restricted to) areas: New paradigms in ACO Theoretical studies in ACO ACO-based evolvable hardware ACO algorithm developments Applications of ACO approaches to new real-world problems Top
	3. Title: Computational Systems Biology Organizers: Till Steiner, Yaochu Jin and Bernhard Sendhoff Computational systems biology targets the computational modeling and analysis of biological processes on a systems level. On a systems level means that the nonlinear interaction between many heterogeneous and functionally diverse components is not ignored but captured on different levels of abstraction. It is expected that the holistic approach might require a less detailed simulation on the level of single functional elements. In any case, the level of abstraction will be governed by the task and sometimes it might be better to "ignore something of everything than everything of something". Computational models are built in order to simulate biological systems, e.g. to verify their expected behaviour and to predict the behaviour if certain system constraints are changed. In order to predict unknown system responses, the model has to capture the essential system organisation and functionality. As is common in model building in any case the simpler model is to be preferred. Another important function of a model is its ability to teach us about the principles of the biological system. Since we are free to simplify and to adapt the model, we are possibly able to observe its bare essentials. This process is a prerequisite for the transfer of wanted properties of biological systems into other scientific domains where different basic system substrates hinder us to simply copy. Computational intelligence and systems engineering are examples, where we would like to transfer system level properties like robustness, flexibility and autonomy to. Closely connected to the modelling, simulation and prediction of biological systems is the structural and functional analysis of experimental data on which in one way or another all models are based. The focus within the domain of computational systems biology is again more on the system level, thus on the data analysis of experimental findings on the networked interaction of many components. It is evident that organisational elements that relate structure to function play an essential role in this approach. Besides advancing our understanding of biological processes, we can envisage at least two direct applications domains of computational systems biology: Medical practice and pharmaceutical industry, and computational intelligence. In both areas, a systems level understanding of the organisation of biological organisms seems to be required, in the first case to make qualitative steps towards new medications which treat the illness holistically, and in the second case towards artificial systems that are finally able to truly realize properties such as robustness, flexibility and autonomy. Topics of interest include but are not limited to: system level biological modelling prediction of model simulations analysis and modelling of gene regulatory networks organisation and architecture of biological systems large scale software systems for biological models and data analysis data mining techniques for bioinformatics information integration for systems biology biochemical networks relation between structural and functional analysis of biological systems genotype - phenotype maps multi-scale and multi-level modelling cytomics: biological organization on the cellular level regulatory vs. functional mechanisms in biological systems transfer of system-level biological properties to computational and technical systems hierarchical system modelling principles of bio-chemical computation applications of computational systems biology Top
	4. Title: Constraint-Handling Techniques Used in Evolutionary Algorithms Organizer: Efrén Mezura-Montes In their original versions, evolutionary algorithms (EAs) lack a mechanism to handle the constraints of an optimization problem. On the other hand, several real-world optimization problems include different types of constraints (e.g. linear, nonlinear, equality and inequality constraints). Therefore, a considerable amount of research has been dedicated to develop mechanisms to incorporate feasibility information into the fitness function of an EA. The most popular technique is the use of penalty functions. The aim is to decrease the fitness of those infeasible solutions in order to favor feasible solutions in the selection process. The main drawback of penalty functions is the careful fine-tuning required by the penalty factors, which determine the severity of the penalization. Based on this limitation, several approaches have been proposed to deal with it and also alternative techniques have been proposed (e.g. separation of constraints and objectives, special representations and operators, hybrid approaches, etc.). The session aims to promote the discussion and presentation of novel works related with the following (but not limited to) issues: New Constraint-Handling techniques Performance measures in evolutionary constrained optimization Comparison of different Constraint-Handling approaches Theoretical studies of EAs in constrained search spaces On-line and self adaptation in Constraint-Handling techniques. Special operators (boundary operators, feasibility-preserve, etc.) Hybrid (global-local) search approaches for constrained optimization Population diversity (feasible and unfeasible solutions) techniques Constraint-Handling techniques in multiobjective optimization Constraint-Handling techniques in real world problems Top
	5. Title: Differential Evolution Organizer: Uday K. Chakraborty Differential evolution (DE), an attractive global optimization method, is a relatively new member of the evolutionary computation family. This method has recently been shown to produce superior results in a wide variety of real-world applications.This special session seeks to highlight the latest developments in this rapidly emerging research area by bringing together researchers and practitioners. Topics of interest include, but are not limited to: Theory of differential evolution Analysis of parameter settings (population size, scale factor, crossover rate) Multi-objective differential evolution Differential evolution for noisy problems Differential evolution for constrained optimization Hybridization (with local search and other soft computing approaches) Application in diverse domains (e.g., digital filter design, clustering, engineering design, bioinformatics, etc.) Connections to / comparison with particle swarm optimization Top
	6. Title: Evolutionary Algorithms Based on Probabilistic Models Organizers: Qingfu Zhang, José Antonio Lozano and Pedro Larrañaga Evolutionary algorithms based on probabilistic models (EAPM) have been recognized as a new computing paradigm in evolutionary computation. Instances of EAPMs include, estimation of distribution algorithms, probabilistic model building genetic algorithms, ant colony optimization, cross entropy methods, to name a few. There is no traditional crossover or mutation in EAPMs. Instead, they explicitly extract global statistical information from their previous search and build a probability distribution model of promising solutions, based on the extracted information. New solutions are sampled from the model thus built. EAPMs represent a new systematic way to solve hard search and optimization problems. The last decade has seen growing interest in this area. As an interdisciplinary research area, the development of EAPMs needs joint efforts from the researchers and practitioners in evolutionary computation, machine learning, statistics and simulation. This special session aims at bringing researchers who are interested in EAPM together to review the current state-of-art, exchange the latest ideas and explore future directions. The major topics of interest include, but are not limited to: Theory of EAPMs New algorithms Combination of machine learning techniques and EAPMs Combination of statistics techniques and EAPMs Combination other heuristics and EAPMs EAPMs for multiobjective optimization problems EAPMs in dynamic environments Parallel implementation of EAPMs Real-world/novel applications Top
	7. Title: Evolutionary Algorithms in Forecasting Support Systems Organizer: Wei-Chiang Samuelson Hong Businesses require accurate forecasts of demand in order to make effective decisions, such as marketing, financial investment, inventory, distribution, human resource planning, purchasing, and so on. These forecasts are usually based on a function combination system (Forecasting support systems; FSS) of traditional statistical methods, evolutionary algorithms, artificial intelligent computation, and management judgment. Although the wide application of FSS concepts, due to lack of abilities to catch the forecast data pattern, FSS resulted in over-reliance on the use of informal judgment and higher expense. With the advantages of evolutionary algorithms computing capabilities over the traditional optimization approaches, recently, they have been applied to catch the data pattern more accurate via systematical computation process, such as genetic algorithms (GA), simulated annealing algorithms (SA), tabu search algorithms (TA), ant colony optimization (ACO), immune algorithm (IA), and particle swarm optimization algorithm (PSO). The objective of this special session is to invite together research and application of evolutionary algorithms for any forecasting fields. Scope: This special session invites contributions in all aspects of applying evolutionary algorithms in any FSS to improve the usage efficiency of those algorithms and aims to promote the discussion and exploration of new ideas. Topics of interests include (but not limited to): The usage of evolutionary algorithms in any FSS. Theoretical comparison of evolutionary algorithms in FSS. Empirical comparison of evolutionary algorithms in FSS. Parameter determination by genetic algorithms (GA) in FSS. Parameter determination by simulated annealing algorithms (SA) in FSS. Parameter determination by tabu search algorithms (TA) in FSS. Parameter determination by ant colony optimization (ACO) in FSS. Parameter determination by immune algorithm (IA) in FSS. Parameter determination by particle swarm optimization algorithm (PSO) in FSS. Other application of novel intelligent evolutionary algorithms in FSS. Top
	8. Title: EC at Work - Generating Value with Evolutionary Computation Organizers: Thorsten Schnier, Andy Pryke and Arthur Kordon Evolutionary Computation is increasingly finding applications in business. This special session will provide a common forum for both business and academic people to report and discuss successful applications of EC in business, with an emphasis on a diverse range of projects in which EC is continuously in use to enhance profitability. This is EC coming out of the laboratory and into offices and factories. By identifying "showcase examples", the session will aid those researchers who wish to see their work in active, commercial use. The case-studies will form a framework for explaining the diversity of EC applications to potential commercial partners and give the academic community information on the difficulties which need to be overcome in order to commercialise EC techniques. We are inviting submissions that demonstrate solid applications of EC which are making a difference to business, trailblaze innovative use of EC in business and share best practices and experiences in real-world problem solving using EC. Unlike other sessions in this conference, the applications presented do not have to use newly developed techniques. Instead, the business implementation should be of interest. We will be looking for papers that show how EC is used to create value in the real world. Also of interest are papers that identify the factors which hinder adoption of EC or enable technical excellence to blossom into business use. We strongly encourage papers with applications based on integration between Evolutionary Computation, Neural Networks, and Fuzzy Logic. Topics of interest include, but are not limited to Online or live EC systems Retail applications Design applications Manufacturing applications Industrial applications Marketing applications EC methods used within products Any Commercial or Business application Surveys of applications Quantification of business effects of EC applications Obstacles to Adoption Unique Selling Points of EC Commercial EC software Top
	9. Title: Evolutionary Computation for Electronic Design Automation Organizer: Giovanni Squillero The special session on Evolutionary Computation for Electronic Design Automation addresses all aspects of evolutionary computation exploited for electronic systems engineering. It covers the design process, test, and tools for design automation of electronic products ranging from integrated circuits to distributed large-scale systems. IEEE already publishes an average of 20 papers per year where evolutionary techniques are used to solve design automation problems. Concurrently, the field of evolutionary computation reveals a significant interest in evolvable hardware and problems such as routing, placement, or test pattern generation. The special session will show the latest developments in the field of evolutionary algorithms applied to design automation. Design and test professionals will confront the challenges the industry faces, and learn how these challenges may be addressed using innovative evolutionary techniques developed in academia. Persons involved in innovative industrial designs are particularly encouraged to submit papers to foster the feedback from design to research. Special Session topics include: Analog circuit design Automatic test pattern generation Built-in self test Design validation Evolutionary design of electronic circuits Evolutionary hardware design methodologies Evolutionary robotics Evolvable hardware Floorplanning Hardware/Software co-design Hybrid evolutionary/exact approach Hardware accelerated methodologies Logic synthesis Production Test Automation Routing Test program generation Top
	10. Title: Evolutionary Computation for Expensive Optimization Problems Organizers: Chi Keong Goh, Kay Chen Tan, Yew Soon Ong and Joshua Knowles The application of evolutionary computation (EC) techniques for optimization problems with high evaluation cost is an increasingly important area of research. Although it has been established that EC techniques are powerful optimization tools, researchers are facing the challenge of increasing computational cost of today’s applications. In particular, computational cost increases with the size, complexity and fidelity of the problem model and the large number of function evaluations involved in the optimization process may be cost prohibitive or impractical without the use of high performance computing resources or computationally cheap surrogate models. Furthermore, the causes of high evaluation cost and its effects on the number of evaluations that can be afforded differ widely from one problem to another, as the following three examples may illustrate. (i) When evolving controllers for a simulated collective of robots, the fidelity of the physics simulator, system uncertainties, and the desire to obtain robots that are robust to rare events may result in long simulation times. (ii) When evolving a novel protein for a specific binding target by synthesis of proteins in vitro and their subsequent screening, thousands of proteins may be synthesized in parallel but each further "generation" will take another 12 hours to process and will also have financial implications. (iii) When evolving a basic conceptual design for a new building, an architect evaluating the designs will suffer fatigue after several hours and will eventually have to stop. This special session invites contributions in all aspects of applying evolutionary computation to the optimization of expensive optimization problems and aims to promote the discussion and exploration of new ideas. Topics of interests include (but not limited to): The use of meta-models and their integration in evolutionary algorithms. The development of distributed or parallel evolutionary algorithms. Evolutionary algorithms using Grid Computing. Evolutionary algorithms using fitness inheritance. Analytical/theoretical studies of on-line and off-line learning for expensive optimization. Validation techniques for approximate landscape models. Empirical comparison of meta-modeling approaches. Statistical performance analyses of surrogate-assisted EAs, to include worst-case performance estimation. Off-line learning and landscape state machines. Incorporating local search or approximated gradient descent into surrogate-assisted EAs. Test functions designed specifically to model particular expensive optimization problems. Expensive optimization problems with noise and/or constraints. Combinatorial and mixed integer expensive optimization problems. Effects of the time taken to perform one evaluation on algorithm choice. Effects of population size limit on algorithm choice. Multiobjective evolutionary methods for expensive optimization problems. Using landscape statistics (e.g. epistasis variance, correlation length, estimates of number of optima) to adapt on-line or off-line search strategies. Design-of-experiments initialization schemes/principles for use in EAs. Operators, selection schemes, niching methods and adaptation schemes for use with EAs using surrogate modeling. Intensification/diversification issues in expensive optimization problems. Top
	11. Title: Evolutionary Computation for Power Engineering Applications Organizers:Wong Kit Po and ZhaoYang Dong Evolutionary computing (EC) includes the well established algorithms such as Genetic Algorithms (GAs), Evolutionary Programming (EP) and Evolutionary Strategies (ES) as well as recent advances such as Particle Swarm Optimisation (PSO) and Differential Evolution (DE). They have been widely applied in many scientific and engineering applications which require search and optimisation. Power systems and control problems are typical complex nonlinear problems often require advanced optimisation tools such as EC. With the advantages of evolutionary computing techniques over the traditional optimisation methods, they have been applied in many power engineering problems such as power system planning, stability assessment, load parameter estimation, power system/electricity market management and control. The objective of this special session is to bring together research and development of evolutionary computation in power engineering areas. The topics of interests are in the general area of (but not limited to) evolutionary computation application in power engineering and control. Top
	12. Title: Evolutionary Computation for Process Optimisation Organizers: Ashutosh Tiwari and Kostas Vergidis There is an ever-increasing demand in the process industry to become more flexible, more responsive and more competitive. Process optimisation is proposed as an attractive solution that provides measurable and tangible results regarding process improvement. Traditional methods often employed to solve complex optimisation problems in process industry tend to inhibit elaborate exploration of the search space, often resulting in sub-optimal solutions. Evolutionary Computation (EC) is generating considerable interest for solving these problems as it is proving robust in delivering global optimal solutions and in resolving the limitations encountered in traditional methods. Most real-world processes (e.g. business processes, industrial processes, etc.) are large scale, high dimensional, non-linear, constrained and involve a variety of uncertain/qualitative factors. While many issues have been addressed in recent research efforts, limitations for wider applications of EC techniques for process optimisation still exist and restrict more realistic solutions to be achieved. Current challenges include, for example, complexity of the search space, nature of constraints and objectives, hierarchical optimisation problems and search within integrated qualitative/quantitative space. The CEC2007 special session on 'Evolutionary Computation for Process Optimisation’ will invite submissions in all areas of evolutionary computation dealing with the challenges of applying EC techniques to both theoretical and real world problems regarding process optimisation. Topics of interest include, but are not limited to: Hierarchical Optimisation Problems Process Optimisation Problems with Multiple Stages Optimisation of High Dimensional Processes Handling Large Number of Constraints and Objectives in Process Optimisation Problems Sequential Process Optimisation Business Process Optimisation Variable Interaction in Process Optimisation Problems Uncertainty in Process Optimisation Problems Complexity of Search Space Integrating Qualitative Knowledge in Process Optimisation Search within Integrated Quantitative/Qualitative Space Multi-disciplinary Process Optimisation Problems EC in Real-world Process Optimisation Problems Top
	13. Title: Evolutionary Computation in Bioinformatics and Computational Biology Organizers: Scott F. Smith and Madhu Chetty Bioinformatics and computational biology present a number of difficult optimization problems with large search spaces. Recent applications of evolutionary computation in this area suggest that they are well-suited to this area of research. This special session will highlight applications of evolutionary computation to a broad range of topics including drug docking, protein folding, sequence alignment, genomics, proteomics, metabolics, medicine, and ecological modeling. Particular interest will be directed towards novel applications of evolutionary computation to problems in these areas. Motivation why the topic is both timely and relevant to the CEC community. The bioinformatics special session has been a part of CEC since 1999 and continues to bring in between 8-16 quality papers. There is a clear interest in both the computational intelligence community and biology communities for this special session. Top
	14. Title: Evolutionary Computation in Cryptology and Computer Security Organizers: Julio Cesar Hernandez, Juan M. Estevez-Tapiador and John A Clark Motivations Techniques taken from the field of Evolutionary Computation (especially Genetic Algorithms, Genetic Programming, Artificial Immune Systems, but also others) are steadily gaining ground in the area of cryptology and computer security. In recent years, algorithms which take advantage of approaches based on Evolutionary Computation have been proposed, for example, in the design and analysis of a number of new cryptographic primitives, ranging from pseudorandom number generators to block ciphers, in the cryptanalysis of state-of-the-art cryptosystems, and in the detection of network attack patterns, to name but a few. There is a growing interest from the cryptographic and computer security communities towards Evolutionary Computation techniques. This has occurred partly as a result of these recent successes, but also because the nature of systems is changing in a way which means traditional computer security techniques will not meet the full range of tasks at hand. The increasing distribution, scale, autonomy and mobility of emerging systems is forcing us to look to nature-inspired computation to help deal with the challenges ahead. There is a general feeling that the area is ripe for further research; the creation of a body of work has only just begun. Objectives The special session encourages the submission of novel research at all levels of abstraction (from the design of cryptographic primitives through to the analysis of security aspects of ‘systems of systems’). We believe the special session will promote further co-operation between specialists in evolutionary computation (and its current partners such as biology), computer security, cryptography and other disciplines, and will give interested researchers an opportunity to review the current state-of-art of the topic, exchange recent ideas, and explore promising new directions. Top
	15. Title: Evolutionary Computation in Defence Applications Organizers: Lam Thu Bui and David Cornforth Applying techniques of evolutionary computation to defence and security domain has recently received considerable attention from the research community with a tremendous demand for effective and robust solutions to defence and security problems. This special session on Evolutionary Computational in Defence Applications (ECDA) seeks to bring together researchers from around the globe for a creative discussion on recent advances and challenges facing ECDA research. The special session on ECDA is organized within CEC’2007. We invite authors to submit their original and unpublished work that demonstrates current research and novel applications in ECDA. The special session will focus on evolutionary computation techniques for problems related to, but not limited to, the following topics: Red teaming War-gaming Mission planning Tactical decision making Path-planning for agents in combat-simulated systems Logistics and supply chain Risk assessments Data farming/mining/fusion Network intrusion detection Adaptive forces Emergence in battlefield simulations Surveillance Information Operations Network centric warfare Unmanned Vehicles and Swarms Top
	16. Title: Evolutionary Computation in Dynamic and Uncertain Environments Organizers: Andries P. Engelbrecht, Shengxiang Yang and Yaochu Jin Many real-world optimization problems are subjected to dynamic and uncertain environments that are often impossible to avoid in practice. For instance, the fitness function is uncertain or noisy as a result of simulation/measurement errors or approximation errors (in the case where surrogates are used in place of the computationally expensive high fidelity fitness function). In addition, the design variables or environmental conditions may also perturb or change over time. For these dynamic and uncertain optimization problems, the objective of the evolutionary algorithm is no longer to simply locate the global optimum solution, but to continuously track the optimum in dynamic environments, or to find a robust solution that operates optimally in the presence of uncertainties. This poses serious challenges to conventional evolutionary algorithms. Handling dynamic and uncertain optimization problems in evolutionary computation has received an increasing research interests over the recent years. A variety of methods have been reported across a broad range of application backgrounds. This special session aims at bringing researchers from academia and industry together to review the latest advances and explore future directions in this field. Topics of interest include but are not limited to: Benchmark problems and performance measures Tracking moving optima Dynamic multi-objective optimization Adaptation, learning, and anticipation Handling noisy fitness functions Using fitness approximations Searching for robust optimal solutions Comparative studies Hybrid approaches Theoretical analysis Real-world applications Top
	17. Title: Evolutionary Computation in Finance and Economics Organizers: Pedro Isasi and Edward Tsang Evolutionary computation is steadily becoming more and more widespread in the fields of finance and economics. It's been proved to be a powerful tool in domains were analytic solutions are not a good alternative. So far it has been successfully used in financial engineering, risk management, portfolio optimization, industrial organization, auctions, experimental economics, financial forecasting, market simulation or agent-based computational economics among many other areas. Topics suitable for the special session include, but are not limited to the above mentioned. The session is open to any high quality submission from researchers working at the intersection of evolutionary computation and economics and finance. Themes of the submitted articles should include the use of evolutionary computation in Economics and Finance, including (but not limited to) the following: Agent-Based Computational Economics Artificial Stock Markets Behavioral Finance Derivative Pricing Evolutionary Games and Industrial Organization Experimental Economics Financial Data Mining Financial Engineering Financial Time Series Forecasting and Analysis Hedging Strategies Portfolio Management Term Structure Model Trading Strategies Simulation of Social Processes Macroeconomics Econometrics Preference, Risk and Uncertainty Environmental Economics Public Economics Microeconomic Behaviour Specific EC techniques include (but are not limited to): Evolution Strategies Evolutionary Programming Genetic Algorithms Associated methods of Genetic Programming and Classifier Systems. Hybrid Evolutionary Systems Swarm Intelligence Bio-inspired Algorithms Evolutionary artificial Neural Networks Top
	18. Title: Evolutionary Computation in Games Organizers: Luigi Barone and Philip Hingston Games have proven to be an ideal test domain for the study of evolutionary algorithms as they provide competitive and dynamic environments that are interesting to observe and fun to play. Evolutionary techniques have successfully been applied to many different kinds of games, however a number of research issues and questions still remain. Papers are invited for this special session which aims to bring together leading researchers and practitioners in this field. Topics of interest include, but are not limited to, the following: Learning in games Coevolution in games Neuro-evolution in games Opponent modelling in games Theoretical or empirical analysis of evolutionary algorithms and representations for games Comparative studies (e.g. evolved players versus human-designed players or other learning algorithms) Multi-agent and multi-strategy learning Evolutionary game theory Board and card games Economic or mathematical games Imperfect information and non-deterministic games Evasion (predator/prey) games 3D computer and console games "Realistic" games for simulation or training purposes Games for mobile platforms Games involving control of physical objects (e.g. remote control car racing) Games involving physical simulation Top
	19. Title: Evolutionary Computation in Manufacturing Systems and Robotics Organizers: S. G. Ponnambalam and S. M. N. Arosha Senanayake Over the past decades, evolutionary computation plays an essential role in advancements of manufacturing systems and robotics. Funding from government agencies and industrial corporations has led to the development of new solutions in areas such as engineering design (e.g., process analysis, design automation, autonomous decision making, and reengineering), manufacturing (e.g., system design, planning and scheduling, reconfigurable systems, e-business, and system diagnosis), and medicine (e.g., disease diagnosis, generation of medical protocols, and discovery of medical knowledge). On the other hand, researchers involved in robotics have achieved significant results by the inclusion of evolutionary concepts, algorithms and computational methods to the robotics systems. Special emphasis has been made in evolvable nature; evolvable software and hardware. Many humanoids came into the market and mostly commercial and attracted in home appliances as well. Further, recent developments of Bio-Inspired Robotics Devices led tremendous impetus in the society. Significant improvements in human health devices, sports performance analysis, injury prevention and in surgery led innovative products for the society. Therefore, the use of evolutionary computation in manufacturing systems and robotics is one of the key criteria behind development of many advanced and smart products in the society. It is our obligation as researchers to provide due recognition by introducing a special session in this congress. The scope is primarily based on the advancements and improvements of above mentioned fields which are: Applying evolutionary algorithms in intelligent manufacturing systems Evolutionary computation in intelligent robotics Evolvable Bio-Inspired Robotics Devices (EBIRDS) Humanoid robots and complex adaptive systems Applications in industry, healthcare, and service organizations. Man-Machine interaction. Top
	20. Title: Evolutionary Computation in Space Organizer: Massimiliano Vasile In the last decade several authors have faced the problem of generating first guess solutions for space related problems through the application of some global optimization technique. Furthermore the inherent multidisciplinary nature of space mission design has required the solution of complex multiobjective optimization problems with mixed variables. Methods derived from evolutionary computation have been used extensively to solve many of these problems ranging from global trajectory optimization to multidisciplinary design of re-usable launch vehicles, from planning and scheduling for autonomous robots to the optimal control of space tether systems. Most of these problems can be modeled only as black-boxes, therefore evolutionary based techniques, although they represent only a portion of the global techniques used in space, are perfectly suited for the solution of these kinds of problems. Not only did this give the way to the application of evolutionary computation but led also to the development of new approaches. In most of the cases basic evolutionary heuristics have been hybridized with other techniques, such as gradient methods or branch and prune methods, or modified to better adapt to the specific application under investigation. This has led to the creation of new heuristics, new meta-heuristics or new hybridizations that have proven to be very effective. This special session intends to collect all the efforts made in the application of evolutionary computation techniques, or related methods, to space problems. Authors are invited to submit papers on one or more of the following topics: Global trajectory optimization Multidisciplinary design for space missions Formation and constellation design and control Optimal control of spacecraft and rovers Planning and scheduling for autonomous systems in space Multiobjective optimization for space applications Resource allocation and programmatics Evolutionary Computation for Concurrent Engineering Distributed global optimization Mission planning and control Mixed variable problems Top
	21. Title: Evolutionary Computation in Structural and Multidisciplinary Optimization Organizers: Kang Tai and Tapabrata Ray A large majority of problems in aerospace, civil, electrical, electronics, manufacturing, materials and mechanical engineering are actually concerned with the geometry of the component or system, hence optimization in these disciplines often involve optimizing the topology, shape and size of structures (the word 'structures' is interpreted in the broadest sense to include problems of geometry, material selection, arrangement of components, network configuration, etc.). In particular, the topology or configuration of a system is a discrete quantity that lends itself more effectively to treatment by evolutionary computation (EC) techniques than by continuous optimization methods. This is so because EC techniques do have inherent advantages when applied to problems where the design variables are discrete or combinatorial in nature. In addition, EC techniques are also advantageous for solving many engineering design analysis and optimization problems where design sensitivity information is not easily available or where the problem require a multidisciplinary (multi-physics, multi-criterion, distributed) approach. Hence, in recent years, there has been much interest in employing EC techniques for tackling problems of structural and multidisciplinary optimization. This special session aims to bring together researchers and practitioners in this field to promote discussion and advance the application of EC in these problems. Papers related to any aspect of this field are therefore invited, including but not limited to the following example topics : structural design optimization topology and shape optimization optimization in forming and other manufacturing processes shape optimization of ducts and manifolds optimization in VLSI/circuit layout and design optimization in thermal management of systems optimization of packaging systems optimal design for folding/unfolding systems optimal trajectory planning for autonomous vehicles optimization of sensor network deployment multidisciplinary design optimization (MDO) Top
	22. Title: Evolutionary Computer Vision Organizers: Vic Ciesielski, Mario Koeppen and Mengjie Zhang Computer vision is a major unsolved problem in computer science and engineering. Over the last decade there has been increasing interest in using evolutionary computation approaches to solve vision problems. Computer vision provides a range of problems of varying difficulty for the development and testing of evolutionary algorithms. The theme of the proposed special session is the use of evolutionary computation for solving computer vision and image processing problems. This special session seeks to highlight the latest developments in this research area by bringing together researchers and practitioners in both evolutionary computation and computer vision. Authors are invited to submit their original and unpublished work to this Special Session. Topics of interest include, but are not limited to: Genetic Algorithms for all aspects of computer vision and image processing, such as object detection, object classification, texture analysis, image segmentation, image feature selection/construction, and robot vision; Genetic programming approaches to all aspects of computer vision and image processing; particle swarm approaches to any aspect of computer vision and image processing; Hybrid evolutionary-neural approaches to computer vision and image processing; Hybrid evolutionary-fuzzy approaches to computer vision and image processing; and Hybrid evolutionary-symbolic learning approaches to computer vision and image processing. Top
	23. Title: Evolutionary Computing for Decentralized Systems Organizers: Anwitaman Datta and Indranil Gupta Evolutionary computing includes well-established as well as maturing tools and methodologies including nature inspired self-adaptive algorithms, complex systems, swarm intelligence, evolutionary game theory and genetic algorithm, and is used in diverse domains. Authors are invited to submit research papers on topics related to evolutionary computing for decentralized systems for a special session in the Congress on Evolutionary Computation (CEC 2007). CEC is the world's premier conference dedicated to evolutionary computation. It brings together researchers and practitioners in the field of evolutionary computation and computational intelligence from all around the globe. It also has special sessions dedicated to specific topics providing a platform for researchers using evolutionary computing techniques in their niche areas. The special session papers go through the same standard of peer reviewing and are published in the CEC conference proceedings. Scope Large-scale networked and decentralized systems such as the Internet, peer-to-peer systems, sensor networks, ad-hoc networks, etc., have grown to be very complex. Such decentralized systems are often characterized by designed or emergent self-organizational properties. Evolutionary principle, both for design and analysis of these systems, is a special subtopic under the general premise of self-organization. Focus decentralized systems of interest include (but are not limited to) the Internet, the semantic web, peer-to-peer systems, sensor networks, ad-hoc and mesh networks, wireless and grid environments. The papers submitted should strictly and mainly be using evolutionary computing to address various issues in such decentralized systems. Papers are solicited for topics including (but not limited to): Systems Design and Optimization Foundations, Theory, and Modeling Empirical Studies, Practical Experiences and Case Studies Complex Adaptive Systems Algorithms: Biologically or Socially Inspired, Swarm and Epidemic Evolutionary Data Mining Evolutionary Games Scalability, Reliability, Security, Mobility Immune systems Topology control Top
	24. Title: Evolutionary Design Organizer: Ian C. Parmee The intention of the Special Session is to explore the integration of evolutionary search, exploration and optimization across a wide spectrum of design activities. The session intends to investigate the manner in which evolutionary computation can be utilized to generate design concepts and achieve meaningful designs in addition to more standard evolutionary optimization processes. The support of decision-making and innovation during preliminary design and novel EC strategies and problem representations that best handle design complexity and support people-centred aspects are also of interest. The development and integration of appropriate evolutionary computing strategies will likely focus upon engineering and architectural design. However, papers relating to other areas such as, for example, drug design and discovery; software design; the design of foodstuffs etc are also most welcome. Papers which identify and address generic design aspects across several such domains that can particularly benefit from EC application / integration and illustrate EC potential in these areas are particularly encouraged. The following areas would be of interest although the call is not limited to them: The integration of both deterministic design approaches and computational intelligence techniques with evolutionary design environments The extraction and processing of evolutionary design data and its visualization within appropriate designer interfaces The application of novel evolutionary computing techniques and strategies that address specific design / analysis problems of high complexity Human-centred aspects and interactive evolutionary design systems Evolutionary search and exploration across uncertain / poorly-defined design environments Supporting innovative and creative design Development and integration of aesthetic fitness measures Multi-objective design satisfaction Search and optimization within heavily constrained design domains Reducing computational expense during detailed design, analysis and optimisation Evolutionary design systems involving high-performance and distributed computing, problem-solving environments and grid-based application Top
	25. Title: Evolutionary Multiobjective Optimization Organizer: Carlos A. Coello Coello Although most real-world problems have several (and normally conflicting) objectives that have to be satisfied at the same time, for the sake of simplicity, we tend to transform all but one of those objectives into constraints in order to simplify the optimization task. Vilfredo Pareto stated in 1896 a concept (known today as "Pareto optimum") that constitutes the origin of research in multiobjective optimization. According to this concept, the solution to a multiobjective optimization problem is normally not a single value, but instead a set of values (also called the Pareto set). The interest of applying evolutionary computation techniques to multiobjective optimization dates back to the 1960s, with Rosenberg's doctoral dissertation. One of the reasons why evolutionary algorithms are so suitable for multiobjective optimization is because they can generate a whole set of solutions (the Pareto set) in a single run rather than requiring an iterative process like traditional mathematical programming techniques. The main aim of this special session organized in the context of the 2007 Congress on Evolutionary Computation (CEC'2007) is to bring together both experts and newcomers working on EMOO to discuss different issues including (among others) the following: Real-world applications of EMOO algorithms Test functions for EMOO algorithms New EMOO techniques Metrics for EMOO algorithms Techniques to keep diversity in the population Comparison of EMOO techniques Theoretical aspects of EMOO algorithms Top
	26. Title: Evolutionary Planning and Scheduling Organizers: Keshav Dahal and Peter Cowling Planning and scheduling problems occur where numerous activities compete for scarce resources (including time). Real-word planning and scheduling problems are generally complex, constrained and multi-objective in nature. Typical examples of such planning and scheduling problems include project planning/scheduling, production planning/scheduling, activities planning, staff rostering, machine scheduling, timetabling, vehicle routing, resource assignment, etc. A sustained research effort over recent years continues to achieve many real world and theoretical successes in the development and application of new techniques for solving planning and scheduling problems. Recently, there has been a high research interest in evolutionary, meta-heuristic and soft computing approaches for solving scheduling problems. This session aims to attract papers on which report on the application of techniques such as these, their refinement for addressing particular planning and scheduling problems, and new theoretical developments. Topics of the interests include, but are not limited to, the following: Planning and scheduling theory, practice and benchmarks for evolutionary and meta-heuristic approaches Effective problem representations for evolutionary planning and scheduling Evaluation of quality of plans and schedules to use for evolutionary and meta-heuristic scheduling Multi-objective evolutionary and meta-heuristic planning and scheduling Dynamic evolutionary and meta-heuristic planning and scheduling Planning and Scheduling under uncertainty using evolutionary and meta-heuristic approaches Hybridization of evolutionary and meta-heuristic methods with other approaches for planning and scheduling Real world applications of evolutionary/meta-heuristic approaches in planning and scheduling Multi-disciplinary applications of evolutionary and meta-heuristic planning and scheduling approaches Top
	27. Title: Evolved Art and Music Organizers: Kevin Seppi and Daniel Ashlock Evolved Art and Music are an emerging discipline within evolutionary computation. While the representation of artistic or musical objects for evolution is challenging, writing fitness functions that can locate artistically meritorious images or melodies is perhaps the greatest challenge. Many evolutionary artists have solved this problem by using human-in-the-loop fitness. Both effective use of human-in-the-loop and machine computed fitness are welcome in submissions to this session. Top
	28. Title: Genetics-Based Machine Learning Organizers: Hai Huong (Helen) Dam, Kamran Shafi and Hussein A. Abbass Since its inception by John Holland, Genetics-Based Machine Learning (GBML) has been investigated for various application domains including biological systems, data mining and engineering applications. This special session will be a forum for discussing current and future directions of GBML. We invite researchers to submit their original and unpublished work including but not limited to the following topics: Continuous Actions in Learning Classifier Systems (LCS) Concept Drift in GBML Distributed and Multi-agent Systems using GBML Evolutionary Decision Trees Evolutionary Ensemble learning Evolutionary LCS Evolutionary Neural Networks Evolutionary Support Vector Machines Foundation of GBML Research GBML for Function Approximation GBML for Noisy Environments GBML for Stream Data Mining GBML for Outlier Detection Online/Offline Learning Real World Applications using GBML Representation Effect in GBML Theory of GBML Top
	29. Title: Incremental Strategies to Computational Intelligence Organizer: Steven Guan In recent years, there are more and more works in the area of incremental strategies to computational intelligence, ‘incremental’ in the sense such that changes can be introduced through the input or output domain during computation. New objectives can be added, deleted, or replaced ruing multi-objective computation. Input or output attributes can be introduced in increments rather than in batch during training. Solutions, strategies and new methodologies to deal with such type of changes have been proposed and developed. Works done showed that the same set of strategies can be applied to the training of neural networks, genetic algorithms, and various computational intelligence methods, with reduced interference. With more and more research results upcoming, a question is raised: incremental strategy - is it a new approach to computational intelligence? We welcome research papers to be submitted in this area. Submission can be done by following the link: http://ieee-cis.org/conferences/cec2007/upload.php, select this special session via the list of research topics on the submission page. Top
	30. Title: Linkage in Evolutionary Computation Organizers: Ying-ping Chen and Meng-Hiot Lim Genetic and evolutionary algorithms (GEAs) are powerful search methods based on the paradigm of evolution and widely applied to solve problems in many disciplines. In order to improve the performance and applicability, numerous sophisticated mechanisms have been introduced and integrated into GEAs in the past decades. One major category of these enhancing mechanisms is the concept of linkage, which models the relation between the decision variables with the genetic linkage observed in biological systems, and linkage learning techniques. Linkage learning connects the computational optimization methodologies and the natural evolution mechsnisms. Not only can learning and adapting natural mechanisms enable us to design better computational methodologies; the insight gained by observing and analyzing the algorithmic behavior permits us to further understand biological systems, based on which GEAs are developed. This special session aims at providing a forum for reviewing of current state-of-art linkage learning techniques, exchanging of ideas and viewpoints on linkage, as well as discussing the future directions. We invite researchers to submit their original and unpublished work including but not limited to the following topics: Linkage in biological systems and computational algorithms Linkage for discrete/continuous variables Linkage processing, handling, and learning techniques Identification and utilization of linkage Adaptation of representation and/or operators for linkage Theoretical aspects of linkage Applications of the linkage concept Position papers Real-world applications Top
	31. Title: Memetic Algorithms Organizers: Ferrante Neri, Yew-Soon Ong, Hisao Ishibuchi and Meng-Hiot Lim One of the recent growing areas in Evolutionary Algorithm (EAs) research is Memetic Algorithms (MAs). MAs are population-based meta-heuristic search methods inspired by Darwinian principles of natural evolution and Dawkins notion of a meme defined as a unit of cultural evolution that is capable of local refinements. Recent studies on MAs have revealed their successes on a wide variety of real world problems. Particularly, they not only converge to high quality solutions, but also search more efficiently than their conventional counterparts. In diverse contexts, MAs are also commonly known as hybrid EAs, Baldwinian EAs, Lamarkian EAs, cultural algorithms and genetic local search. The aim of this special session is to reflect the most recent advances in the field, and increase the awareness of the computing community at large on this effective technology. In particular, we endeavor to demonstrate the current state-of-the-art in the theory and practice of MAs. Topics of interests include (but are not limited to): novel competitive, collaborative and cooperative frameworks of MAs analytical and/or theoretical studies that enhance our understanding of the behaviors of MAs using multiple memes or local searchers or exact methods adaptive MAs (e.g., meta-Lamarckian and meta-Baldwinian) hybridizations with exact and/or approximate methods asymptotic global convergence analyses and/or complexity analyses of MAs optimization in discrete, continuous and dynamic problems using MAs multi-objective optimization using MAs real-world applications of MAs. Top
	32. Title: Molecular Computing for Information Processing and Self-Assembly Organizers: Zuwairie Ibrahim, Danny van Noort, and John A. Rose Molecular computing is a rapidly growing, interdisciplinary field, which focuses on the discovery of biopolymer-based techniques for applications in biotechnology, computation, nanotechnology, engineering, and bioinformatics. Papers relating to recent advances in molecular computing for information processing and self-assembly are sought, including (but not restricted to): Information processing in vitro and in vivo Modeling and simulation of molecular computing, in silico DNA-based memories Error and efficiency assessment and optimization Microfluidic information networks DNA self-assembly and nanotechnology Theoretical models of DNA information processing Output visualization and readout of DNA-based computers Models for disease diagnosis Of particular interest are submissions reporting experimental and/or simulation results. Top
	33. Title: New Particle Swarm Optimization Methods Organizers: Andries Engelbrecht and Frans van den Bergh Much progress have been made in the past decade in developing new particle swarm optimization (PSO) approaches to improve the performance of the original methods. Much research efforts are still directed towards improving the performance of PSO. These improvements attempt to increase accuracy, reduce computational time, address the trade-off between exploration and exploitation, amongst others. A number of improvements have also been developed that use PSO in hybrid with other methods. The focus of this special session is on new PSO methods, and applications of these methods. Topics considered for the special session include: Performance criteria to be used to quantify the performance of PSO methods Improvements to existing PSO methods New PSO methods for unconstrained optimization Methods for multi-objective optimization, niching, constrained optimization, dynamic environments Hybrid methods where PSO is combined with other optimization methods Mechanisms to maintain diversity, and to balance the explorationexploitation trade-off Self-adaptive PSO methods Benchmarking of PSO algorithms Applications of PSO approaches to new real-world problems Top
	34. Title: Organic Computing – An Approach to Controlled Emergence? Organizers: Christian Müller-Schloer and Hartmut Schmeck Organic Computing has emerged as a challenging vision for future information processing systems. Organic Computing is based on the insight that we will soon be surrounded by large collections of autonomous systems, which are equipped with sensors and actuators, aware of their environment, communicating freely, and organizing themselves in order to perform the actions and services that seem to be required. The presence of networks of intelligent systems in our environment opens fascinating application areas but, at the same time, bears the problem of their controllability. Hence, we have to construct such systems - which we increasingly depend on - as robust, safe, flexible, and trustworthy as possible. In particular, a strong orientation towards human needs as opposed to a pure implementation of the technologically possible seems absolutely central. In order to achieve these goals, our technical systems will have to act more independently, flexibly, and autonomously, i.e. they will have to exhibit life-like properties. We call those systems "organic". Hence, an "Organic Computing System" is a technical system, which adapts dynamically to the current conditions of its environment. It will be self-organizing, self-configuring, self-optimizing, self-healing, self-protecting, self-explaining, and context-aware. First steps towards adaptive and self-organizing computer systems are already being undertaken. Adaptivity, reconfigurability, emergence of new properties, and self-organization are topics in a variety of research projects. The German Research Foundation (DFG) granted a considerable amount of funding to start a priority research program on Organic Computing. It addresses fundamental challenges in the design of Organic Computing systems; its objective is a deeper understanding of emergent global behavior in self-organizing systems and the design of specific concepts and tools to support the construction of Organic Computing systems for technical applications. The goal of this proposed workshop is to further spread the ideas of Organic Computing within the international research community. We expect a fruitful mutual exchange: Organic Computing might help to solve some of the challenges faced in Evolutionary Computation, while ideas like adaptivity, reconfigurability and self-organization in many cases require techniques covered by Evolutionary Computation. This workshop will provide a forum to present the current status of research in Organic Computing and discuss challenges and future directions for research and development Suggested topics for contributions to this workshop include but are not limited to: self-organization and emergent behavior self-organization in production and logistics bio-inspired computing multi-agent systems and cellular automata autonomic computing complex adaptive systems self-organization in biological systems artificial life technical usage and controllability of emergence Top
	35. Title: Performance Assessment of Multi-Objective Optimization Algorithms Organizers: Kalyanmoy Deb, P. N. Suganthan and Eckart Zitzler Problem definitions, codes, etc. are available from http://www.ntu.edu.sg/home/epnsugan By participating in this special session, you will also participate in the CEC2007 competitions. For more details, please click here. Optimization for multiple conflicting objectives results in more than one optimal solutions (known as Pareto-optimal solutions). Although one of these solutions is to be chosen at the end, the recent trend in evolutionary and classical multi-objective optimization studies have focused on approximating the set of Pareto-optimal solutions. It is then believed that such a set of solutions will collectively provide a good insight to the different trade-off regions on the Pareto-optimal front, thereby aiding a better and more confident decision making at the end. However, which type of approximation of the Pareto-optimal set is sought strongly depends on the decision maker; here, various aspects such as convergence to the Pareto-optimal front and maintenance of diversity among the obtained solutions come into play. Thus, to assess the performance of such optimization algorithms, the decision maker's preferences need to be taken into account. Evolutionary multi-objective optimization (EMO) methodologies were suggested in the early Nineties for this task, and since then a number of performance assessment methods have been suggested. Most of the existing simulation studies comparing different EMO methodologies are based on specific performance measures. After more than 10 years of research and development of efficient EMO algorithms, we realize that it is time to evaluate the existing EMO and classical generating methodologies on carefully chosen test problems and practical problems which are scalable with respect to the objectives and the decision variables. The goal is to consider different types of preferences, e.g., formalized in terms of appropriate performance measures, so as to bring out the essential features needed in an algorithm to efficiently solve multi-objective optimization problems depending on the decision maker's preferences. The comparisons will be made for a limited number of overall evaluations, so that the existing or new algorithms can be evaluated for different functional abilities: i) to meet well specified preferences (convergence to Pareto front, diversity, objective values, etc.) ii) to scale well on many objectives, and iii) to scale well on many variables. Following the successful organizations of two other special sessions on unconstrained and constrained single-objective optimization (held in CEC-05 and scheduled in CEC-06, respectively), during CEC-07, we shall organize this special session on multi-objective optimization algorithms. We shall develop a set of scalable test problems providing different kinds of complexities, a set of different, commonly considered preference types following the recent literature, a careful plan for execution of simulations and a presentation format, so interested participants can put to test their already published or modified algorithms. We hope to publish the edited volume as Springer's Lecture Notes in Computer Science after the conference. With this background, we now invite you give your feedbacks / suggestions on developing a test suite with appropriate evaluation methods and would like to know if you would be willing to participate in this exercise. Any sort of search engine is allowed, including hybrids with mathematical programming techniques as well as different metaheuristics. Please could you kindly send an email to all the organizers with the following details? ------------------------------------------------------------------------------------------------------------------------------- Name: Email: URL: Methods to be used: (a) EMO (b) Classical Generating Method (c) Hybrid If you know of researchers who might be interested in making contribution(s), please kindly provide names/email addresses. Thank you. ------------------------------------------------------------------------------------------------------------------------------- The test functions are available from http://www.ntu.edu.sg/home/EPNSugan (under “CEC’07 Session”). Paper submission deadline is 10 April 2007. Top
	36. Title: Quantum Computing and Quantum Computational Intelligence Organizers: Marek Perkowski, Xiaoyu Song, William Hung, Guowu Yang, Dmitri Maslov and Faisal Khan Historically, classical computer concepts and underlying technologies have been invented by mathematicians and physicists rather than engineers. It were engineers, however, who took basic concepts and ideas and created the practical powerful and inexpensive computers of today. We believe that the same will happen in case of quantum computers. A new area of engineering - quantum computer engineering - will be created to solve many engineering aspects of future quantum circuits and computers. At the present time there are several research groups and conferences in the field of quantum computing, quantum circuits and quantum information that are addressed to physicists, mathematicians and theoretical computer scientists. Very recently, the first ever commercial quantum computer Orion (adiabatic quantum computing) has been demonstrated by D-Wave company from Vancouver, British Columbia and there are many signs that commercialization of quantum computing and information ideas will take place sooner rather than later. There is a growing group of researchers with engineering background who do active research in the area of what will become quantum computer engineering and there are universities who teach already “quantum engineers”. The research areas that will be discussed in this special session include: Design principles of new quantum computers and methodologies, especially adiabatic quantum computers, quantum walks and one –way quantum computers. Synthesis and automated synthesis of quantum circuits - a quantum equivalent of traditional CAD of logic synthesis. The research includes using Genetic Algorithm, Genetic Programming and other evolutionary and biology mimicking methods to synthesize quantum circuits and optimize them. Programming languages and environments for quantum synthesis and algorithms. Quantum Computational Intelligence - all learning and problem-solving models known from Computational Intelligence such as Neural Nets, Bayes nets, Logic Networks, Fuzzy Logic, state machines, etc can be extended to those based on quantum circuits and automata. Quantum game theory, applications of quantum games. There is a special interest in Grover algorithm and its applications to solve NP-hard problems. New algorithms for search implemented on various types of quantum computers Design, testing and verification of practical quantum circuits, including quantum neural nets using various realization technologies. Using GA, GP and other evolutionary paradigms in all areas of quantum circuits, quantum information and quantum computing. Top
	37. Title: Recent Developments in Artificial Immune Systems Organizers: Jonathan Timmis and Emma Hart The immune system is a remarkably complex interacting network of cells. There are many day to day challenges facing the immune system, such as the vast array of stimuli that can infect the host, the continual bombardment of such stimuli (there is no resting for the immune system) and the countless interactions that occur with other processes and systems within the host (such as the neural systems and endocrine or hormonal systems). The remarkable ability if the immune system to react to these stimuli (antigens) and remove the majority of them from our system has fascinated researchers over the years. This immune system has inspired researchers in the area of Artificial Immune Systems (AIS) over the past 11 years to develop a wide range of algorithms inspired by various aspects of immunology. Within AIS, there is no one standard AIS algorithm, however, there are a number of basic flavours of AIS algorithms that draw their inspiration from certain processes within the immune system. To date there are clonal selection, immune network, bone marrow and negative selection algorithms. There are many variations on these algorithms, but there is at least some basic acceptance, for example, of what a clonal selection algorithm consists of and how it should work. The aim of this session is to consolidate state of the art in AIS, but also to encourage the publication of more ‘mould breaking’ AIS research. Particular encouragement is given to the submission of applications of AIS in industrial settings and advances in theoretical aspects of AIS. To maintain the interdisciplinarity of AIS, the session encourages the submission of immune modelling results using both computational and mathematical modelling techniques that can inform the development of AIS. In addition, we welcome position papers which provide a discussion of current “hot” topics in the area, for example outlining future directions for the area, or discuss the current state-of-the art. Papers are invited for submission on unpublished work in the following (but not restricted to) areas: AIS algorithm developments New paradigms in AIS Theoretical studies in AIS Applications of AIS Modelling of immune system components and processes Position papers Top
	38. Title: Theoretical Foundations of Evolutionary Computation Organizers: Benjamin Doerr and Frank Neumann Evolutionary computation methods such as evolutionary algorithms or ant colony optimization have been shown to be very successful when dealing with real-world applications or problems from combinatorial optimization. The theoretical understanding of these, in practice successful, algorithms is an important topic and has gained increasing interest in recent years. The aim of this special session is to bring together people working on theoretical aspects of evolutionary computation. We will use the following definition for a theory paper which should make clear the scope of this special session. A theory paper considers a particular randomized search heuristic on a particular problem or class of problems. It contains formally stated theorems which are proven rigorously. It is not enough to validate a model or a theory by experiments. Top

Topics of interest include, but are not limited to